贝索斯致股东信(2018)

qimoe 发布于 2 个月前

致我们的股东:

在过去的20年间有一个很值得关注的现象。看看下面这些数字:

1999 3%

2000 3%

2001 6%

2002 17%

2003 22%

2004 25%

2005 28%

2006 28%

2007 29%

2008 30%

2009 31%

2010 34%

2011 38%

2012 42%

2013 46%

2014 49%

2015 51%

2016 54%

2017 56%

2018 58%

这个百分比显示了第三方商家销售的比例,他们大都是中小型商户,这个值在20年间从3%增至58%。简单说:

第三方商家一直在打脸亚马逊自营商品,啪啪的打

这比你想象的更可怕因为这么些年亚马逊自营商品也在惊人增长,从16亿增至1.17千亿,在这个时期平均每年自营的增长率是25%。而第三方商家从1亿增至1.6千亿,平均每年增长率是52%。拿一个外部的数据来比较,eBay在这个期间的年增长率是20%,从28亿增至950亿。

为什么独立商家在亚马逊卖的比eBay好?为什么独立商家在面对这么强大的亚马逊自营商品还能增长更快?这没有单一答案,但我们确实知道一个重要的部分:

我们通过打造我们能想到的最好的销售工具来帮助第三方商家来打败我们的自营商品。这种工具有很多,比如帮助他们管理库存的,处理支付的,追踪物流的,生成报告的和跨境销售的——我们每年都在创造更多的工具。但是最最重要的是FBA和Prime会员项目。在两者的组合下,这两个项目显著的提升了用户从第三方卖家购买的体验。现在看起来这两个项目都很成功,可是人们在我们推出这两个服务当时可不是这么想的。我们冒着很大的财务风险和内部的杂音持续的投资这两个项目,而在这些年,我们还在其他想法里持续的大量投入。我们不能预测这些项目的最终形态,更不用说知道它们能否成功,但这些项目都是我们依靠直觉用心推进的,并且持续对他们抱有信心。

直觉,好奇心,以及“闲逛(wandering)”的力量

在亚马逊很早期的时候,我们就知道我们要打造一个建造者(builder)的文化——大家要有好奇心、爱探索。他们要喜欢创新,即便是个专家了也要像新手一样抱有好奇心。这种建造者的思维帮助我们在面对很大很困难的机会的时候能够保持一种低姿态的信念:创造、尝试、重新创造、再尝试,不断循环,我们就一定能干成。

有些时候,你知道你自己在做什么,你可以很高效,树立一个目标然后执行。相反,到处“闲逛”的工作就不会特别高效。但是这也不是随机闲逛,而是被直觉、好奇心和以用户为核心的精神指引的,即便前路漫漫,也觉得值得。“闲逛”是高效很重要的一种反向平衡,你两者都需要有。那些颠覆式的创新,“非线性”的那种,就是来自于“闲逛”。

AWS的数百万用户覆盖了从初创公司到大公司,从政府到非盈利机构,每一个都在为了自己的用户打造更好的解决方案。我们花了很长时间思考这些机构/公司,以及里面的人都在想什么——开发者,研发经理,运营经理,CIO,chief digital officer,chief information security officer等等

我们在AWS中做的很多工作都是基于倾听用户。问用户他们想要什么至关重要,认真听他们的答案,然后想出一个计划又快又好的提供他们所需。没有这种对用户的关注,任何业务都做不下去。但是这还不够。最大的机会是用户还不知道的需求,我们必须站在他们的角度为他们创造需求。这就需要我们的想象力了。

AWS本身就是一个例子。没有人需要AWS。但事实证明世界上正需要一个像AWS一样的东西,只不过大家都不知道罢了。我们有一个直觉,然后是好奇心,我们承担了必要的财务风险,然后开始建造——返工,实验,以及无数次迭代。

在AWS内部,这个模式也发生了很多次。比如,我们打造了DynamoDB,一个延展性非常强的低延迟key-value数据库,现在它正服务几千用户。在倾听用户方面,我们听到他们抱怨商业数据库又贵又难用,于是我们就做了Amazon Aurora(译者注:此处有重复Aurora的硬广,不清楚Aurora性能的可以参考之前来信)。

但是我们对专业领域的专业数据库也很有信心。在过去的20到30年间,公司们基本都在用关系型数据库来运行它们的服务。由于程序员对这种数据库都很熟悉,所以这么些年也就将就过来了。尽管没那么好,但是本来数据量也不太大,对于时延要求也不高,所以也能凑活用。可是如今,很多应用都需要大量数据(TB到PB级别)的实时存取,在保证低时延的情况下还得做到每秒处理几百万请求。这个时候不仅仅有DynamoDB,还有in-memory数据库Amazon ElastiCache,series数据库Amazon Timestream,和Ledger解决方案Amazon Quantum Ledger Database——不同的工作用合适的工具省钱又高效。

我们还在关注如何让公司更好地利用机器学习。我们在这上面已经干了很长时间,并且和其他的创新项目类似,我们最初的几次想把机器学习的工具接口化的尝试都失败了。在很多年的“闲逛”之后,经历了不断的实验、迭代和打磨,参考了很多用户的意见之后,我们终于做出了SageMaker。这个功能在18个月前发布,SageMaker去掉了机器学习里面每一步复杂的概念和细节,真正让人人都能使用AI。如今,几千用户都在基于AWS的SageMaker功能来打造他们的机器学习应用。我们会持续升级这项应用,增添增强学习(reinforcement learning)的新功能。增强学习有一个陡峭的学习曲线和很多可移动动的部分,这种学习难度让只有为数不多的大公司才能完全理解并应用起来。如果没有以用户为中心为用户创造的精神,冕我有好奇心和坚韧不拔的态度,就不会有这些应用。并且用户很买账,目前AWS已经可以带来每年300万的营收,而且还在快速增长。

想象不可能

亚马逊今天还仍旧只在零售市场占据非常小的份额,只有个位数百分比,在我们运营的各个国家都有比我们更大的零售商。这很大程度上是因为90%的零售页还停留在线下,冰冷的泥瓦仓库中。我们思考了很多年怎么服务实体商家,但是我们觉得必须要能够先创造一些能在实体环境下打动用户的东西,于是就有了Amazon Go无人超市。我们在这之后有了清晰的目标——干掉排队。没人喜欢排队,你应该走进商店,拿上你想要的东西,然后就直接离开。

想做到这一步非常困难,尤其是技术上,需要全世界几百个聪明又专业计算机科学家和工程师共同的努力。我们需要打造我们自己的专利摄像头和货架,并为此开发全新的计算机视觉算法,包括如何把几百个摄像头采集到的画面联系到一起。我们还得让这种技术让人看不见,而不会影响购物体验。结果用户很喜欢,他们把Amazon Go的购物体验形容为“魔性(magical)”我们现在在Chicago, San Francisco and Seattle有10家店,未来还会有更多。

失败也得扩大规模

在公司成长的过程中,任何事情都得扩大规模,包括失败的规模。如果你失败的规模没有增长,你就不可能在现有的体量下进行有效的创新。Amazon会以适合公司规模的方式来进行实验,如果我们面临的是几十亿级别的失败,我们肯定不会武断地进行这种尝试。我们会好好下注,但不是我们所有下场的赌局都会有好结果。这种能够进行高风险尝试的能力正是我们这种大公司能带给消费者和社会的福利。对股东的好消息就是一次成功的收益就能填补过去的很多失败。

Fire phone和Echo的开发几乎开始于同样的时间。当Fire phone失败的时候,我们能够迅速吸取教训(以及回收开发人员)投入到Echo和Alexa的开发中,加速研发过程。对于Echo和Alexa的最初想法是来源于星战的电脑,也来源于我们“闲逛”式的探索了很多年的两个领域:机器学习和云计算。Amazon的早期,人工智能算法主要应用于产品推荐;而AWS的发展给了我们很好的云基础。在多年的开发之后,Echo在2014年面世,有AWS的Alexa在背后支持。

没有用户喊着要Echo,这绝对是我们“闲逛”的产物。市场调研是不管用的。如果你回到2013年问大家:“你需要一个跟小品客薯片盒大小的圆柱形黑盒子,把它放在厨房跟你聊天和给你播音乐吗?”我敢说大家都会一脸看智障一样的看着你说:“不了谢谢,我还有事,先走了……”

从第一代Echo计算,用户已经购买了超过1000万台Alexa设备了。去年我们将Alexa理解请求和做出回答的能力提升了20%。同时我们为Alexa增加了数十万事实性知识来让它变得更博学。开发者们让Alexa的技能数量翻倍,超过了8万个。用户在2018年和2017年相比,和Alexa的交互次数增加了100万次以上。现在已经有超过150个产品内置了Alexa,从耳机、PC到汽车和智能家居,还有更多的设备在路上!

在结束前再说最后一件事。正如我在20多年前第一封致股东信中所说,我们致力于招聘和保留住能像拥有者一样思考的有天赋的人才。达到这个目标需要在我们的员工身上大量投入,就如同Amazon在其他业务上一样,我们不仅仅依靠分析数据,更依靠我们的直觉和我们的心来找到方向。

去年我们把最低薪水增加到了15美金每小时,对于全美的全职、兼职、零工、季节工都一样。这次提薪照顾到了超过25万亚马逊员工,还有10万季节工,他们在上个圣诞假期在全美的亚马逊打工。我们坚定地认为在员工身上投入对于我们是很好的。但这不是驱动我们做出这样决定的最大原因。我们曾经给出的也是很有竞争力的薪水,但是这次我们打算领先他们,提供一个远高于“有竞争力”的薪资。我们这么做是因为我们觉得这么做是对的。

今天我正式喊话挑战我们最大的零售竞争对手(对就是说你!),你们也应该把员工福利提高到我们一样的水平15美金,甚至更高,开到16美金,这样把皮球再踢给我们。这种竞争能让所有人都获利。

我们向员工推出的其他项目也一样发自真心。我在之前提到过Career Choice的项目,就是那个无论员工想学什么我们都报销95%的费用的项目。目前超过16000员工都薅了羊毛,这个数字还在不断增长。和这个相似,我们Career Skills项目帮助我们的小时工学习关键的职场技能,诸如如何写简历、如何沟通、如何使用计算机的基本功能等等。在去年10月,作为我们这个承诺的延续,我们签署了President’s Pledge to America’s Workers(总统对美国劳工的誓言),并宣布我们要让至少5万美国劳工的技能通过我们创新的培训项目得到提升。

我们的投资不仅仅针对我们现在的员工。为了培训未来的劳动力,我们承诺投资5千万在我们最近宣布的“美国未来工程师”项目上,这个项目旨在提高美国小中大学生STEM和SC的教育水平,尤其是关注于吸引女孩和“弱势群体(minorities)”来加入这个行业。我们招收老兵人才的项目也在持续进行,就快达成在2021年招募25000个退伍老兵和家属的目标。通过Amazon Technical Veterans Apprenticeship项目,我们为这些老兵提供云计算相关的技术培训,让他们能够适应亚马逊的环境,快速进入岗位。

给我们的用户一个大大的感谢,感谢你们愿意以更高的标准要求我们来为你们服务;也要感谢我们股东们的不懈支持;感谢我们的员工的努力和拼劲。亚马逊的所有员工都在倾听用户并从用户的角度不断创新(原句为Teams all across Amazon are listening to customers and wandering on their behalf. 译者注:此处的wandering我翻译成了创新,其实是上文所提到的那个“闲逛”的概念)

一如既往,附了信,Day 1.

Jeffrey P. Bezos

(Vinchent翻译)


英文原文

To our shareowners:

Something strange and remarkable has happened over the last 20 years. Take a look at these numbers:

1999 3%

2000 3%

2001 6%

2002 17%

2003 22%

2004 25%

2005 28%

2006 28%

2007 29%

2008 30%

2009 31%

2010 34%

2011 38%

2012 42%

2013 46%

2014 49%

2015 51%

2016 54%

2017 56%

2018 58%

The percentages represent the share of physical gross merchandise sales sold on Amazon by independent third-party sellers – mostly small- and medium-sized businesses – as opposed to Amazon retail’s own first party sales. Third-party sales have grown from 3% of the total to 58%. To put it bluntly:

Third-party sellers are kicking our first party butt. Badly.

And it’s a high bar too because our first-party business has grown dramatically over that period, from $1.6 billion in 1999 to $117 billion this past year. The compound annual growth rate for our first-party business in that time period is 25%. But in that same time, third-party sales have grown from $0.1 billion to $160 billion – a compound annual growth rate of 52%. To provide an external benchmark, eBay’s gross merchandise sales in that period have grown at a compound rate of 20%, from $2.8 billion to $95 billion.

Why did independent sellers do so much better selling on Amazon than they did on eBay? And why were independent sellers able to grow so much faster than Amazon’s own highly organized first-party sales organization? There isn’t one answer, but we do know one extremely important part of the answer:

We helped independent sellers compete against our first-party business by investing in and offering them the very best selling tools we could imagine and build. There are many such tools, including tools that help sellers manage inventory, process payments, track shipments, create reports, and sell across borders – and we’re inventing more every year. But of great importance are Fulfillment by Amazon and the Prime membership program. In combination, these two programs meaningfully improved the customer experience of buying from independent sellers. With the success of these two programs now so well established, it’s difficult for most people to fully appreciate today just how radical those two offerings were at the time we launched them. We invested in both of these programs at significant financial risk and after much internal debate. We had to continue investing significantly over time as we experimented with different ideas and iterations. We could not foresee with certainty what those programs would eventually look like, let alone whether they would succeed, but they were pushed forward with intuition and heart, and nourished with optimism.

Intuition, curiosity, and the power of wandering

From very early on in Amazon’s life, we knew we wanted to create a culture of builders – people who are curious, explorers. They like to invent. Even when they’re experts, they are “fresh” with a beginner’s mind. They see the way we do things as just the way we do things now. A builder’s mentality helps us approach big, hard-to-solve opportunities with a humble conviction that success can come through iteration: invent, launch, reinvent, relaunch, start over, rinse, repeat, again and again. They know the path to success is anything but straight.

Sometimes (often actually) in business, you do know where you’re going, and when you do, you can be efficient. Put in place a plan and execute. In contrast, wandering in business is not efficient … but it’s also not random. It’s guided – by hunch, gut, intuition, curiosity, and powered by a deep conviction that the prize for customers is big enough that it’s worth being a little messy and tangential to find our way there. Wandering is an essential counter-balance to efficiency. You need to employ both. The outsized discoveries – the “non-linear” ones – are highly likely to require wandering.

AWS’s millions of customers range from startups to large enterprises, government entities to nonprofits, each looking to build better solutions for their end users. We spend a lot of time thinking about what those organizations want and what the people inside them – developers, dev managers, ops managers, CIOs, chief digital officers, chief information security officers, etc. – want.

Much of what we build at AWS is based on listening to customers. It’s critical to ask customers what they want, listen carefully to their answers, and figure out a plan to provide it thoughtfully and quickly (speed matters in business!). No business could thrive without that kind of customer obsession. But it’s also not enough. The biggest needle movers will be things that customers don’t know to ask for. We must invent on their behalf. We have to tap into our own inner imagination about what’s possible.

AWS itself – as a whole – is an example. No one asked for AWS. No one. Turns out the world was in fact ready and hungry for an offering like AWS but didn’t know it. We had a hunch, followed our curiosity, took the necessary financial risks, and began building – reworking, experimenting, and iterating countless times as we proceeded.

Within AWS, that same pattern has recurred many times. For example, we invented DynamoDB, a highly scalable, low latency key-value database now used by thousands of AWS customers. And on the listening carefully-to-customers side, we heard loudly that companies felt constrained by their commercial database options and had been unhappy with their database providers for decades – these offerings are expensive, proprietary, have high-lock-in and punitive licensing terms. We spent several years building our own database engine, Amazon Aurora, a fully-managed MySQL and PostgreSQL-compatible service with the same or better durability and availability as the commercial engines, but at one-tenth of the cost. We were not surprised when this worked.

But we’re also optimistic about specialized databases for specialized workloads. Over the past 20 to 30 years, companies ran most of their workloads using relational databases. The broad familiarity with relational databases among developers made this technology the go-to even when it wasn’t ideal. Though sub-optimal, the data set sizes were often small enough and the acceptable query latencies long enough that you could make it work. But today, many applications are storing very large amounts of data – terabytes and petabytes. And the requirements for apps have changed. Modern applications are driving the need for low latencies, real-time processing, and the ability to process millions of requests per second. It’s not just key-value stores like DynamoDB, but also in-memory databases like Amazon ElastiCache, time series databases like Amazon Timestream, and ledger solutions like Amazon Quantum Ledger Database – the right tool for the right job saves money and gets your product to market faster.

We’re also plunging into helping companies harness Machine Learning. We’ve been working on this for a long time, and, as with other important advances, our initial attempts to externalize some of our early internal Machine Learning tools were failures. It took years of wandering – experimentation, iteration, and refinement, as well as valuable insights from our customers – to enable us to find SageMaker, which launched just 18 months ago. SageMaker removes the heavy lifting, complexity, and guesswork from each step of the machine learning process – democratizing AI. Today, thousands of customers are building machine learning models on top of AWS with SageMaker. We continue to enhance the service, including by adding new reinforcement learning capabilities. Reinforcement learning has a steep learning curve and many moving parts, which has largely put it out of reach of all but the most well-funded and technical organizations, until now. None of this would be possible without a culture of curiosity and a willingness to try totally new things on behalf of customers. And customers are responding to our customer-centric wandering and listening – AWS is now a $30 billion annual run rate business and growing fast.

Imagining the impossible

Amazon today remains a small player in global retail. We represent a low single-digit percentage of the retail market, and there are much larger retailers in every country where we operate. And that’s largely because nearly 90% of retail remains offline, in brick and mortar stores. For many years, we considered how we might serve customers in physical stores, but felt we needed first to invent something that would really delight customers in that environment. With Amazon Go, we had a clear vision. Get rid of the worst thing about physical retail: checkout lines. No one likes to wait in line. Instead, we imagined a store where you could walk in, pick up what you wanted, and leave.

Getting there was hard. Technically hard. It required the efforts of hundreds of smart, dedicated computer scientists and engineers around the world. We had to design and build our own proprietary cameras and shelves and invent new computer vision algorithms, including the ability to stitch together imagery from hundreds of cooperating cameras. And we had to do it in a way where the technology worked so well that it simply receded into the background, invisible. The reward has been the response from customers, who’ve described the experience of shopping at Amazon Go as “magical.” We now have 10 stores in Chicago, San Francisco, and Seattle, and are excited about the future.

Failure needs to scale too

As a company grows, everything needs to scale, including the size of your failed experiments. If the size of your failures isn’t growing, you’re not going to be inventing at a size that can actually move the needle. Amazon will be experimenting at the right scale for a company of our size if we occasionally have multibillion-dollar failures. Of course, we won’t undertake such experiments cavalierly. We will work hard to make them good bets, but not all good bets will ultimately pay out. This kind of large-scale risk taking is part of the service we as a large company can provide to our customers and to society. The good news for shareowners is that a single big winning bet can more than cover the cost of many losers.

Development of the Fire phone and Echo was started around the same time. While the Fire phone was a failure, we were able to take our learnings (as well as the developers) and accelerate our efforts building Echo and Alexa. The vision for Echo and Alexa was inspired by the Star Trek computer. The idea also had origins in two other arenas where we’d been building and wandering for years: machine learning and the cloud. From Amazon’s early days, machine learning was an essential part of our product recommendations, and AWS gave us a front row seat to the capabilities of the cloud. After many years of development, Echo debuted in 2014, powered by Alexa, who lives in the AWS cloud.

No customer was asking for Echo. This was definitely us wandering. Market research doesn’t help. If you had gone to a customer in 2013 and said “Would you like a black, always-on cylinder in your kitchen about the size of a Pringles can that you can talk to and ask questions, that also turns on your lights and plays music?” I guarantee you they’d have looked at you strangely and said “No, thank you.”

Since that first-generation Echo, customers have purchased more than 100 million Alexa-enabled devices. Last year, we improved Alexa’s ability to understand requests and answer questions by more than 20%, while adding billions of facts to make Alexa more knowledgeable than ever. Developers doubled the number of Alexa skills to over 80,000, and customers spoke to Alexa tens of billions more times in 2018 compared to 2017. The number of devices with Alexa built-in more than doubled in 2018. There are now more than 150 different products available with Alexa built-in, from headphones and PCs to cars and smart home devices. Much more to come!

One last thing before closing. As I said in the first shareholder letter more than 20 years ago, our focus is on hiring and retaining versatile and talented employees who can think like owners. Achieving that requires investing in our employees, and, as with so many other things at Amazon, we use not just analysis but also intuition and heart to find our way forward.

Last year, we raised our minimum wage to $15-an-hour for all full-time, part-time, temporary, and seasonal employees across the U.S. This wage hike benefitted more than 250,000 Amazon employees, as well as over 100,000 seasonal employees who worked at Amazon sites across the country last holiday. We strongly believe that this will benefit our business as we invest in our employees. But that is not what drove the decision. We had always offered competitive wages. But we decided it was time to lead – to offer wages that went beyond competitive. We did it because it seemed like the right thing to do.

Today I challenge our top retail competitors (you know who you are!) to match our employee benefits and our $15 minimum wage. Do it! Better yet, go to $16 and throw the gauntlet back at us. It’s a kind of competition that will benefit everyone.

Many of the other programs we have introduced for our employees came as much from the heart as the head. I’ve mentioned before the Career Choice program, which pays up to 95% of tuition and fees towards a certificate or diploma in qualified fields of study, leading to in-demand careers for our associates, even if those careers take them away from Amazon. More than 16,000 employees have now taken advantage of the program, which continues to grow. Similarly, our Career Skills program trains hourly associates in critical job skills like resume writing, how to communicate effectively, and computer basics. In October of last year, in continuation of these commitments, we signed the President’s Pledge to America’s Workers and announced we will be upskilling 50,000 U.S. employees through our range of innovative training programs.

Our investments are not limited to our current employees or even to the present. To train tomorrow’s workforce, we have pledged $50 million, including through our recently announced Amazon Future Engineer program, to support STEM and CS education around the country for elementary, high school, and university students, with a particular focus on attracting more girls and minorities to these professions. We also continue to take advantage of the incredible talents of our veterans. We are well on our way to meeting our pledge to hire 25,000 veterans and military spouses by 2021. And through the Amazon Technical Veterans Apprenticeship program, we are providing veterans on-the-job training in fields like cloud computing.

A huge thank you to our customers for allowing us to serve you while always challenging us to do even better, to our shareowners for your continuing support, and to all our employees worldwide for your hard work and pioneering spirit. Teams all across Amazon are listening to customers and wandering on their behalf!

As always, I attach a copy of our original 1997 letter. It remains Day 1.

Sincerely,

Jeffrey P. Bezos

Founder and Chief Executive Officer

Amazon.com, Inc.